Modern computing networks can include multiple computing devices that work together as a coherent group. For example, multiple computing devices can work together to provide a fault-tolerant service in the event that one of the computing devices has an error or otherwise becomes unavailable. One example is the use of replicated state machines to provide a fault-tolerant service. In this approach, multiple state machines in a cluster maintain replicated data shards to provide continuous service in the even that one or more of the state machines has an error or otherwise becomes unavailable. A computing node can include a switch (e.g., a router) or an end point (e.g., a host device, server, etc.).
A consensus algorithm, such as the Raft consensus algorithm, can be used to manage replicating data amongst the data shards stored by each computing node. A consensus algorithm involves multiple data shards communicating to agree on values to make a decision. For example, in the context of replicated state machines, each data shard in the cluster can maintain a log of commands that the state machine takes as input and the consensus algorithm can be used to replicate the log amongst the data shards in the cluster.
A consensus algorithm can require that one data shard from each cluster be designated as the leader of the cluster. The leader of the cluster can be in charge of organizing data replication across the data shards in the cluster. For example, using the raft algorithm, log entries can flow from the leader to the other data shards, thereby causing higher resource consumption by the computing node storing the leader. A computing node can store multiple data shard and each data shard can be a member of a different cluster. As a result, a computing node can store multiple shards that are designated as the leader of their respective cluster. This can result in higher resource consumption by the computing node and cause performance issues.